Understanding student pathways in context-rich problems

Antonenko, Pavlo
Ogilvie, Craig
Niederhauser, Dale
Jackman, John
Kumsaikaew, Piyamart
Marathe, Rahul
Ryan, Sarah
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This paper describes the ways that students’ problem-solving behaviors evolve when solving multi-faceted, context-rich problems within a web-based learning environment. During the semester, groups of two or three students worked on five physics problems that required drawing on more than one concept and, hence, could not be readily solved with simple “plug-and-chug” strategies. The problems were presented to students in a data-rich, online problem-based learning environment that tracked which information items were selected by students as they attempted to solve the problem. The students also completed a variety of tasks, like entering an initial qualitative analysis of the problem into an online form. Students were not constrained to complete these tasks in any specific order. As they gained more experience in solving context-rich physics problems, student groups showed some progression towards expert-like behavior as they completed qualitative analysis earlier and were more selective in their perusal of informational resources. However, there was room for more improvement as approximately half of the groups still completed the qualitative analysis task towards the end of the problem-solving process rather than at the beginning of the task when it would have been most useful to their work.


This is a post-peer-review, pre-copyedit version of an article published in Education and Information Technologies. The final authenticated version is available online at DOI: 10.1007/s10639-010-9132-x. Posted with permission.